Mobile and wireless inertial sensor platform for motion capturing in stroke rehabilitation sessions

Brückner, Hans-Peter; Nowosielski, Rochus; Kluge, Henning; Blume, Holger

doi:10.1109/iwasi.2013.6576085

Cited by 10 publications

(10 citation statements)

References 12 publications

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“…Based on the abstracts we selected papers discussing rehabilitation technologies (at home or the clinic) and excluded papers discussing prevention, prosthetics, coaching and information/educational applications for smartphones that do not have a direct use during rehabilitation, as well as robotic system. Eventually 19 papers were selected [11][12][13][14][15][16][17][18][19][20][21][22][24][25][26][27][28][29][30][31]. They are categorized as follows: a) Wearable posture & movement training systems that have potential in rehabilitation applications by monitoring posture and movements;…”

Section: Methodsmentioning

confidence: 99%

“…In order to capture motion in home based stroke rehabilitation sessions a mobile and wireless inertial platform is proposed [15], called Institute of Microelectronic Systems Inertial Measurement Unit (IM)2SU. The sensor fusion on an 8-bit MCU is enabled by algorithmic modifications of the utilized Kalman filter.…”

Section: Wearable Systems To Monitor Movement and Track Posturementioning

confidence: 99%

“…Table 1 summarizes wearable systems reviewed in this section that are aimed at monitoring movement or posture with respect to the body parts they refer to, the technology used, and results regarding their accuracy. The state of the art in wearable sensing of motion and posture appears to have exceeded that of human observers; or example, [15] argue that the angular resolution of human perception ranges between 2°-6°, which suggests its potential in the context of rehabilitation where still most monitoring and feedback relies on therapists. Developments are very fast, and there are as yet no direct comparison studies regarding the performance of these systems.…”

Section: Wearable Systems To Monitor Movement and Track Posturementioning

confidence: 99%

“…A large number of experimental systems and even some commercial products have been developed for upper extremity training, supporting rehabilitation for several patient groups: stroke, spinal cord injury, multiple sclerosis, cerebral palsy patients, etc. [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] reflecting the large need for effective upper extremity rehabilitation technologies.…”

Section: Introductionmentioning

confidence: 99%

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Literature review on wearable systems in upper extremity rehabilitation

Wang

Chen

Markopoulos

2014

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

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This paper reports a structured literature survey of research in wearable technology for upper-extremity rehabilitation, e.g., after stroke, spinal cord injury, for multiple sclerosis patients or even children with cerebral palsy. A keyword based search returned 61 papers relating to this topic. Examination of the abstracts of these papers identified 19 articles describing distinct wearable systems aimed at upper extremity rehabilitation. These are classified in three categories depending on their functionality: movement and posture monitoring; monitoring and feedback systems that support rehabilitation exercises, serious games for rehabilitation training. We characterize the state of the art considering respectively the reported performance of these technologies, availability of clinical evidence, or known clinical applications.

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Section: Methodsmentioning

confidence: 99%

Section: Wearable Systems To Monitor Movement and Track Posturementioning

confidence: 99%

Section: Wearable Systems To Monitor Movement and Track Posturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Literature review on wearable systems in upper extremity rehabilitation

Wang

Chen

Markopoulos

2014

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

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“…One reason for the growing interest is the improved quality and the reduced costs of the hardware [9]. Another reason is the rising number of applications.…”

Section: Introductionmentioning

confidence: 99%

Multi-sensor Acceleration-Based Action Recognition

Baumann

Schulz

Rosenhahn

2014

Lecture Notes in Computer Science

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Abstract. In this paper, a framework to recognize human actions from acceleration data is proposed. An important step for an accurate recognition is the preprocessing of input data and the following classification by the machine learning algorithm. In this paper, we suggest to combine Dynamic Time Warping (DTW) with Random Forest. The intention of using DTW is to pre-process the data to eliminate outliers and to align the time series. Many applications require more than one inertial sensor for an accurate prediction of actions. In this paper, nine inertial sensors are deployed to ensure an accurate recognition of actions. Further, sensor fusion approaches are introduced and the most promising strategy is shown. The proposed framework is evaluated on a self-recorded dataset consisting of six human actions. Each action was performed three times by 20 subjects. The dataset is publicly available for download.

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